Deep-Learning-based SIRR Methods  [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} solve the reflection removal problem by designing proper deep network architectures, loss functions and exploiting external real or synthetically generated training datasets.
The Cascaded Edge and Image Learning Network (CEILNet) [1]} consists of two cascaded CNN networks, i.e., E-CNN and I-CNN for edge prediction and image reconstruction, respectively. In [3]}, exclusion loss, perceptual loss and adversarial loss are proposed to regularize the learning of the reflection separation network. In [4]}, a bidirectional network (BDN) which consists of a cascaded deep network has been proposed to estimate the reflection image and use it to improve the estimation of the transmission image.
ERRNet [6]} proposes to utilize misaligned training data with an alignment-invariant loss. In [7]}, an Iterative Boost Convolutional LSTM Network (IBCLN) has been proposed to progressively separate the reflection-contaminated image into two image layers. In [8]}, a dual-stream decomposition network has been proposed to enable information exchange at different branches and achieved state-of-the-art single image reflection removal performance.
